
#importing libraries
!pip uninstall networkx -y
!pip install networkx==2.4
# downgrading to networkx to avoid any conflict with scipy version
import numpy as np #importing numpy
import pandas as pd #importing pandas
import matplotlib.pyplot as plt #importing matplotlib for graph visualization
import networkx as nx #importing networkx for network analysis


from google.colab import files #uploading files from local
uploaded=files.upload() ##uploading files from local

Saving Keyword.csv to Keyword (7).csv


keyword_network_analysis = pd.read_csv('Keyword.csv') #reading the dataset
keyword_network_analysis.index+=1 #incrementing the index by 1 because by default the index are generated from 0
keyword_network_analysis #displaying the raw dataset which is unfiltered.
#In the next segment, dataset is refined.


keyword_network_analysis.dropna(subset=['Keyword 1'], inplace = True) #removing null values beginning from column 1, because while checking, automatically it checks from column 1.
#Thus, susbet is keyword 1
keyword_network_analysis #displaying the refined data after dropping null values


keyword_data = keyword_network_analysis.set_index('Title').T.to_dict('list') #new variable named keyword_data which performs computation for the above refined data stored in keyword_network_analysis


analysis = keyword_network_analysis.iloc[:,1:] #new variable named analysis which stores keyword_network_analysis beginning from column 1


analysis.iloc[0,:] #location

Keyword 1                           EQUITY
Keyword 2         ORGANIZATIONAL sociology
Keyword 3                      PERFORMANCE
Keyword 4                    META-analysis
Keyword 5                    PSYCHOMETRICS
Keyword 6          ORGANIZATIONAL research
Keyword 7            FINANCIAL performance
Keyword 8                    AGENCY theory
Keyword 9     ORGANIZATIONAL effectiveness
Keyword 10         ORGANIZATIONAL behavior
Keyword 11            CORPORATE governance
Keyword 12                             NaN
Name: 3, dtype: object


keywords_unique = [] #new variable named keywords_unique to store empty list
for columns in analysis:#for loop to iterate a new variable named columns based on analysis- which stores the computation for the refined data
  for key in analysis[columns].dropna(): #for loop to iterate a new variable named key based on analysis and removing null
    if key not in keywords_unique: #if condition to check whether key is not in keywords_unique
      keywords_unique.append(key) #if key is not present in keywords_unique, then append key and keywords_unique


adjacency_matrix = np.zeros((len(keywords_unique), len(keywords_unique)), dtype = int) #new variable named adjacency_matrix to calculate adjacency_matrix on length of keywords_unique


for i in range(0, 248): #for loop to iterate i in range 0 to 248
  for j in range(0, 248): #for loop to iterate j in range 0 to 248
    if i != j : #if condition to check i is not equal to j
      if (adjacency_matrix[i][j] == 0) and (adjacency_matrix[j][i] == 0) : #if condition to check if adjacency_matrix[i][j] is equal to 0 and also adjacency_matrix[j][i] equals 0
        for t in keyword_data.keys():#for loop to iterate t in keyword_data
          if (keywords_unique[i] in (keyword_data[t])) and (keywords_unique[j] in (keyword_data[t])):#if condition to operate for keywords_unique[i] and keywords_unique[j] based on keyword_data[t]
            adjacency_matrix[i][j] = adjacency_matrix[i][j] + 1 #computing adjacency matrix
            adjacency_matrix[j][i] = adjacency_matrix[j][i] + 1 #computing adjacency matrix


name_columns= keywords_unique #new variable named name_columns which stores keywords_unique
name_rows= keywords_unique #new variable named name_rows which stores keywords_unique


weighted_network = nx.from_numpy_matrix(adjacency_matrix, parallel_edges=False) #new variable named weighted_network to read adj. matrix and compute the weighted network


plt.figure(figsize=(40,25)) #defining size of the plot
graph_position = nx.spring_layout(weighted_network) #new variable named graph_position that represents the position of the weighted_network
graph_edges = weighted_network.edges() #new variable named graph_edges that represents the edges of the weighted_network
nx.draw_networkx_nodes(weighted_network, graph_position, node_size=25, node_color='magenta', alpha=0.6) #drawing the network based on nodes having weighted_network, position of graph, color and size of node and aplha
nx.draw_networkx_edges(weighted_network, graph_position,width=0.5) #drawing the network based on edges having weighted_network, position of graph, and width
nx.draw_networkx_labels(weighted_network, graph_position, font_size=7, font_family='monospace',font_color='orange') #drawing the network based on labels having weighted_network, position of graph, color and size of node and aplha
plt.show() #displaying the network graph


node_degree = weighted_network.degree() #new variable named node_degree that stores degree of weighted_network
strength_compute = weighted_network.degree (weight = 'weight') #new variable named strength_compute that holds weighted_network.degree (weight = 'weight')


#computing degree
columns= ['TotalNodes', 'Degree'] #passing nodes and degree to columns
df_for_degree = pd.DataFrame(node_degree, columns = columns) #creting new dataframe that stores node_degree
df_for_words = pd.DataFrame(keywords_unique, columns = ['Keywords']) #creting new dataframe that stores keywords_unique having columns keywords
df_degwords = pd.merge(df_for_words, df_for_degree, left_index=True, right_index=True) #merging both the dataframes and assigning it to a new variable named df_degwords
df_degwords[['Keywords','Degree']].sort_values(by =['Degree'], ascending=False).head(10) #displaying the merged dataframe and sorting by values for top 10 nodes


#computing strength
df_strength = pd.DataFrame(strength_compute, columns = ['TotalNodes', 'Strength']) #creting new dataframe that stores strength_compute
df_res = pd.merge( df_degwords, df_strength, how="inner", on=['TotalNodes']) #merging both the dataframes( df_degwords,df_strength) and assigning it to a new variable named df_res
df_res[['Keywords','Strength']].sort_values(by =['Strength'], ascending=False).head(10) #displaying the merged dataframe and sorting by values for top 10 nodes


feature_data = pd.DataFrame() #new dataframe named feature_data to add the final data
for i in range(0, 248): #for loop to iterate i from range 0 to 248
  for j in range(0, 248): #for loop to iterate j from range 0 to 248
    if weighted_network.get_edge_data(i, j): #if condition to check for weighted_network.get_edge_data(i, j)
      data_row = { #new variable named data_row
          'Nd1': int(i), #working for node 1
          'Nd2': int(j), #working for node 2
          'Keyword-1':df_degwords.iat[int(i),0], #working for keyword 1
          'Keyword-2':df_degwords.iat[int(j),0], #working for keyword 2
          'Total_Weight': weighted_network.get_edge_data(i,j)['weight'] #computing total weight
      }
      feature_data=feature_data.append(data_row,ignore_index=True) #appending feature_data and data_row


feature_data[['Keyword-1','Keyword-2','Total_Weight']].sort_values(by='Total_Weight', ascending=False).head(10) #displaying the dataframe based on weights for keyword 1 and keyword 2


feature_data_res = pd.merge( df_for_degree, df_strength, how="inner", on=['TotalNodes']) #merging df_for_degree and df_strength on nodes and assigning it to a new variable named feature_data_res
final = feature_data_res[['Degree','Strength']].groupby('Degree').mean().reset_index() #new variable named final which stores feature_data_res[['Degree','Strength']] and grouping based on degree.
final.index+=1 #incrementing the index by 1
final #displaying the degree and the strength, based on which the final graph will be plotted.


#plotting the graph
plt.figure(figsize=(10,8)) #defining size of the plot
plt.title('Analysis of Degree vs Average Strength') #assigning the title to the graph plot
plt.xlabel('Degree') #title for x axis
plt.ylabel('Average Strength') #assigning title for y axis
plt.scatter(final['Degree'], final['Strength']) #scatter plot, passing final['Degree'] on x axis and final['Strength'] on y axis
plt.show() #displaying the graph


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import nltk
import seaborn as sns
nltk.download('stopwords')
nltk.download('punkt')
from nltk.corpus import stopwords

[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!
[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Package punkt is already up-to-date!


##############################
# since the 2020 file has data from 2016 to 2020, we are reading the files starting from 2020 till 2022
##############################

ti=[]
for i in range(2020,2023):
  path = str(i) + ".csv"
  fd = pd.read_csv(path)

  ti.append(fd)

  frame = pd.concat(ti, axis=0, ignore_index=True)


df=frame.copy() # create a copy of dataframe to work on


df = df.astype(str).apply(lambda x: x.str.encode('ascii', 'ignore').str.decode('ascii')) # remove emojis
df['tweet'] = df['tweet'].str.replace('(\@\w+.*?)',"") # remove all the mentions of accounts
df["tweet"] = df["tweet"].str.replace(r'\s*https?://\S+(\s+|$)', ' ').str.strip() # remove links starting with https
df["tweet"] = df["tweet"].str.replace(r'\s*http?://\S+(\s+|$)', ' ').str.strip()  # remove links starting with http
df['tweet'] = df['tweet'].str.replace('(\#\w+.*?)',"") # remove all hashtag mentions
df['tweet'] = df['tweet'].str.replace(r'[^\w\s]+', '') # remove all the remaining special characters
df['tweet'] = df['tweet'].str.replace('amp','') # remove amp word for &
df['tweet'] = df['tweet'].str.strip() # remove any empty spaces

<ipython-input-24-9ac7525f828b>:2: FutureWarning: The default value of regex will change from True to False in a future version.
  df['tweet'] = df['tweet'].str.replace('(\@\w+.*?)',"") # remove all the mentions of accounts
<ipython-input-24-9ac7525f828b>:3: FutureWarning: The default value of regex will change from True to False in a future version.
  df["tweet"] = df["tweet"].str.replace(r'\s*https?://\S+(\s+|$)', ' ').str.strip() # remove links starting with https
<ipython-input-24-9ac7525f828b>:4: FutureWarning: The default value of regex will change from True to False in a future version.
  df["tweet"] = df["tweet"].str.replace(r'\s*http?://\S+(\s+|$)', ' ').str.strip()  # remove links starting with http
<ipython-input-24-9ac7525f828b>:5: FutureWarning: The default value of regex will change from True to False in a future version.
  df['tweet'] = df['tweet'].str.replace('(\#\w+.*?)',"") # remove all hashtag mentions
<ipython-input-24-9ac7525f828b>:6: FutureWarning: The default value of regex will change from True to False in a future version.
  df['tweet'] = df['tweet'].str.replace(r'[^\w\s]+', '') # remove all the remaining special characters


# since the file 2020 has objects from 2016, we are filtering the data as per the question
df['year'] = pd.DatetimeIndex(df['date']).year
df = df.loc[df['year'] > 2016]


# remove stop words
stop = stopwords.words('english')
df['tweet'] = df['tweet'].apply(lambda x: ' '.join([word.lower() for word in x.split() if word.lower() not in (stop)]))


# removing any empty lines based on the tweet column
df['tweet'] = df['tweet'].replace(r'^s*$', float('NaN'), regex = True)
df.dropna(subset=['tweet'], inplace=True)


df1=df[['year','tweet']]
df1.tweet=df1.tweet.map(nltk.word_tokenize).tolist() # tokenize tweets
df1

/usr/local/lib/python3.8/dist-packages/pandas/core/generic.py:5516: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self[name] = value


df2 = df1.explode('tweet') # separating all the tweets to rows
df2


# calculating top 10 words for each year

df2['num'] = 1
df3 = df2.groupby(['year','tweet'],as_index=False)['num'].sum()

fi=[]
for i in range(2017,2023):

  dtf = df3.loc[df3['year'] == i]
  dtf = dtf.sort_values(by=['num'],ascending = False)
  dtf = dtf.head(10)
  fi.append(dtf)

  top10 = pd.concat(fi, axis=0, ignore_index=True)


top10.sort_values(by=['year','num'],ascending=False) # print top 10 words


# plot histogram for each year

for i in range(2017,2023):
  dat = df3.loc[df3['year'] == i]
  sns.histplot(data = dat, x = "num",bins= range(0,100,10))
  plt.title("Histogram for frequency of words for the year - " + str(i),fontweight="bold", fontsize=15)
  plt.show()


df5 = df3.copy()


df5['rank'] = df5.groupby(['year'])['num'].rank(ascending=False,method='first')
df5.sort_values(by=['year','num'],ascending=False)


# calculating total word frequency of each year
df6 = df3.groupby(['year'],as_index=False)['num'].sum()
df6 = df6.rename(columns={'num': 'total'})
df6


df7 = df5.merge(df6,on=['year'],how='left') # add the total count to each word
df7['term frequency'] = df7['num'] / df7['total'] # calculate term frequency by zipf's law

df7


# scalling to rank and term frequency to log
df8=df7.copy()
df8['log_tf'] = np.log(df8['term frequency'])
df8['log_rank'] = np.log(df8['rank'])


# plot log-log plots of word frequencies and rank for each year
sns.set(rc={'figure.figsize':(11,11)})
sns.lineplot(data = df8,x='log_rank',y="log_tf", hue = 'year',palette='hls')
plt.title(' log-log plots of word frequencies and rank for each year',fontweight="bold")
plt.show()


gram=df[['year','tweet']]
gram['bigrams'] = df1['tweet'].apply(lambda x: list(nltk.ngrams(x,2))) # calculating bigrams

<ipython-input-39-e6b0bbcee79a>:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  gram['bigrams'] = df1['tweet'].apply(lambda x: list(nltk.ngrams(x,2))) # calculating bigrams


bic = gram[['year','bigrams']]
bic = bic.explode('bigrams') # separate the bigrams to different rows
bic.dropna(subset=['bigrams'],inplace=True) # dropping any empty rows


# plotting bigram network graphs for each year
def networkgraph(thresh,year):

  bic3 = bic.loc[bic['year'] == year] # filter on  year
  bic3 = bic3[['bigrams']]

  bic3.reset_index(drop=True, inplace=True)
  bic4 = pd.DataFrame(bic3["bigrams"].to_list(), columns=['word1', 'word2']) # separating the bigrams to different columns
  bic4['num'] = 1
  bic4 = bic4.groupby(['word1','word2'],as_index=False)['num'].sum() # count pairs

  bic4.sort_values(by='num',ascending=False)
  bic4 = bic4.loc[bic4['num'] >=thresh] # consider pairs with count more than threshold
  G = nx.from_pandas_edgelist(bic4,source='word1',target='word2') # create graph from dataframe

  plt.figure(figsize=(40,30)) # set figure size
  nx.draw(G,with_labels= True) # plot the graph


networkgraph(2,2017) # network graph for year 2017


networkgraph(2,2018) # network graph for year 2018


networkgraph(2,2019) # network graph for year 2019


networkgraph(2,2020) # network graph for year 2020


networkgraph(2,2021) # network graph for year 2021


networkgraph(2,2022) # network graph for year 2022

	Title	Keyword 1	Keyword 2	Keyword 3	Keyword 4	Keyword 5	Keyword 6	Keyword 7	Keyword 8	Keyword 9	Keyword 10	Keyword 11	Keyword 12
3	Meta-Analyses of Financial Performance and Equ...	EQUITY	ORGANIZATIONAL sociology	PERFORMANCE	META-analysis	PSYCHOMETRICS	ORGANIZATIONAL research	FINANCIAL performance	AGENCY theory	ORGANIZATIONAL effectiveness	ORGANIZATIONAL behavior	CORPORATE governance	NaN
4	Home Country Environments, Corporate Diversifi...	DIVERSIFICATION in industry	BUSINESS planning	PERFORMANCE standards	EMPLOYEES -- Rating of	CORPORATE culture	STRATEGIC planning	ORGANIZATIONAL effectiveness	MANAGEMENT science	MANAGEMENT research	PRODUCT management	NaN	NaN
5	Safeguarding Investments in Asymmetric Interor...	INTERORGANIZATIONAL relations	INTERGROUP relations	BUSINESS communication	INVESTMENTS	SUPPLY chains	KNOWLEDGE management	INTERORGANIZATIONAL networks	CORPORATE governance	GROUP decision making	INTELLECTUAL capital	NaN	NaN
6	Managerialist and Human Capital Explanations f...	EXECUTIVE compensation	WAGES	HUMAN capital	LABOR economics	PERSONNEL management	MANAGEMENT science	CONTINGENCY theory (Management)	COMPENSATION management	EXECUTIVE ability (Management)	CORPORATE governance	NaN	NaN
7	Bidding Wars Over R&D-Intensive Firms: Knowled...	KNOWLEDGE management	INFORMATION resources management	MANAGEMENT information systems	BREAK-even analysis	DATA mining	MANAGEMENT science	RESEARCH & development	RESEARCH & development contracts	CORPORATE governance	DECISION making	ORGANIZATIONAL behavior	TRANSACTION costs
8	When “The Show Must Go On”: Surface Acting and...	EMOTIONS (Psychology)	INTERPERSONAL relations	STRESS (Psychology)	SOCIAL interaction	SOCIAL psychology	EMPLOYEES -- Attitudes	CUSTOMER services	CUSTOMER satisfaction	JOB stress	PEER review (Professional performance)	NaN	NaN
9	Relationships among Supervisors' and Subordina...	SUPERVISORS	JUSTICE	CONFLICT management	MEDIATION	EMPLOYEES	INDUSTRIAL relations	ORGANIZATIONAL behavior	UNITED States -- National Guard	ORGANIZATIONAL effectiveness	DECISION making	RESOURCE allocation	NaN
10	Punctuated Equilibrium and Linear Progression:...	INDUSTRIAL relations	MANAGEMENT science	DECISION theory	ORGANIZATIONAL sociology	PUNCTUATED equilibrium (Evolution)	ORGANIZATIONAL change	ORGANIZATIONAL behavior	ORGANIZATIONAL structure	BUSINESS models	ORGANIZATIONAL research	NaN	NaN
14	The Relationship between Overconfidence and th...	DECISION making	EXECUTIVES	INDUSTRIAL management	NEW products	HIGH technology industries	NaN	NaN	NaN	NaN	NaN	NaN	NaN
15	Governance Through Ownership: Centuries of Pra...	CORPORATE governance	INDUSTRIAL management	STOCKHOLDERS wealth	INSTITUTIONAL investors	WAGES	NEW products	ORGANIZATIONAL structure	ORGANIZATIONAL behavior	DECENTRALIZATION in management	ORGANIZATIONAL effectiveness	NaN	NaN
16	Strategic Satisficing? A Behavioral-Agency The...	EXECUTIVES	STOCKHOLDERS wealth	STOCK repurchasing	CORPORATIONS -- Finance	INCENTIVES in industry	CORPORATE governance	STRATEGIC planning	EXECUTIVE ability (Management)	AGENCY theory	ORGANIZATIONAL behavior	ORGANIZATIONAL effectiveness	NaN
17	Exploring the Agency Consequences of Ownership...	FAMILY-owned business enterprises	DEBT	DIRECTORS of corporations	AGENCY theory	ORGANIZATIONAL behavior	ORGANIZATIONAL structure	EMPLOYEE ownership	CORPORATE governance	DECISION making	BOARDS of directors	INDUSTRIAL relations	NaN
18	Institutional Ownership Differences and Intern...	INSTITUTIONAL investors	DIVERSIFICATION in industry	BUSINESS planning	GLOBALIZATION	BOARDS of directors	INTERNATIONAL business enterprises	FOREIGN investments	PENSION trusts	HIGH technology	STRATEGIC planning	TECHNOLOGICAL innovations	INNOVATION adoption
19	Ownership Structures and R&D Investments of U....	RESEARCH & development	INVESTMENTS	PROPERTY	INCENTIVES in industry	AGENCY theory	ORGANIZATIONAL sociology	ORGANIZATIONAL structure	STEWARDS	NaN	NaN	NaN	NaN
20	The Determinants of Executive Compensation in ...	FAMILY-owned business enterprises	CHIEF executive officers	EXECUTIVE compensation	BUSINESS enterprises	RISK	MUNICIPAL corporations	CORPORATE governance	RESEARCH & development	ORGANIZATIONAL behavior	ORGANIZATIONAL structure	NaN	NaN
21	Ownership Structure, Expropriation, and Perfor...	PROPERTY	PERFORMANCE	STOCKHOLDERS	PROFIT	MINORITY stockholders	EMINENT domain	ORGANIZATIONAL effectiveness	ORGANIZATIONAL structure	CORPORATE governance	NaN	NaN	NaN
22	CEO Stock Options: The Silent Dimension of Own...	STOCK options	STOCKS (Finance)	CHIEF executive officers	STOCK ownership	EXECUTIVE compensation	EMPLOYEE stock options	ORGANIZATIONAL structure	ORGANIZATIONAL effectiveness	DECISION making	RISK management in business	NaN	NaN
26	Assessing Creativity in Hollywood Pitch Meetin...	MANAGEMENT science	DECISION making	SCREENWRITERS	CREATIVE ability	CREATIVE ability in business	SOCIAL judgment theory (Communication)	MOTION picture authorship	SELF-perception	ORGANIZATIONAL behavior	QUALITY of products	NaN	NaN
27	Reactions to Perceived Inequity in U.S. and Du...	INTERORGANIZATIONAL relations	INDUSTRIAL organization	ORGANIZATIONAL behavior	ORGANIZATIONAL effectiveness	INTERGROUP relations	ORGANIZATIONAL structure	BUSINESS networks	SUPPLIERS	STRATEGIC alliances (Business)	NaN	NaN	NaN
28	The Impact of Community Violence and an Organi...	AGGRESSION (Psychology)	VIOLENCE	SOCIAL psychology	ORGANIZATIONAL justice	WORK environment	INDUSTRIAL relations	MANAGEMENT science	VIOLENCE in the workplace	ANGER in the workplace	EMPLOYEES -- Attitudes	PROBLEM employees	WORK attitudes
29	Explaining New CEO Origin: Firm Versus Industr...	CHIEF executive officers	PERSONNEL changes	SUCCESSION planning	EXECUTIVE succession	MANAGEMENT science	EXECUTIVES -- Recruiting	STRATEGIC planning	MANAGEMENT research	EXECUTIVE ability (Management)	JOB qualifications	ORGANIZATIONAL change	NaN
30	Do High Job Demands Increase Intrinsic Motivat...	MENTAL fatigue	JOB stress	INDUSTRIAL psychology	BURNOUT (Psychology)	SOCIAL networks	PERSONNEL management	MANAGEMENT science	MOTIVATION (Psychology)	INTRINSIC motivation	JOB qualifications	ORGANIZATIONAL behavior	ORGANIZATIONAL effectiveness
31	Organizational Hiring Patterns, Interfirm Netw...	PERSONNEL management	PERSONNEL changes	MANAGEMENT science	INTERORGANIZATIONAL relations	CONTAGION (Social psychology)	TEAMS in the workplace	EXECUTIVES -- Recruiting	EMPLOYEE recruitment	ORGANIZATIONAL sociology	BUSINESS networks	INTERORGANIZATIONAL networks	NaN
32	The Effects of Centrifugal and Centripetal For...	PRODUCT management	NEW products	PROBLEM solving	QUALITY of products	DECENTRALIZATION in management	MARKETING management	MANAGEMENT science	PRODUCT design	PRODUCT lines	PRODUCT information management	ORGANIZATIONAL behavior	NaN
33	A Social Capital Model of High-Growth Ventures	SOCIAL capital (Sociology)	INFRASTRUCTURE (Economics)	VENTURE capital	INVESTMENTS	GOING public (Securities)	COMPETITIVE advantage	ENTREPRENEURSHIP	CAPITAL market	RESOURCE management	ORGANIZATIONAL effectiveness	NaN	NaN
37	Transforming Work-Family Conflict into Commitm...	ORGANIZATIONAL behavior	MULTILEVEL marketing	ORGANIZATIONAL commitment	MARKETING management	QUALITY of work life	JOB satisfaction	AMBIVALENCE	ORGANIZATIONAL structure	ORGANIZATIONAL effectiveness	ORGANIZATIONAL sociology	NaN	NaN
38	Advocacy, Performance, and Threshold Influence...	NEW products	PERFORMANCE evaluation	COMMERCIAL products	PRODUCT management	MARKETING	DECISION making	MARKETING -- Decision making	RESEARCH & development	STRATEGIC planning	PRODUCT design	NaN	NaN
39	Managing from the Boundary: The Effective Lead...	LEADERSHIP	TEAMS in the workplace	STRATEGIC planning	SELF-management (Psychology)	MANAGEMENT -- Employee participation	CRITICAL incident technique	TASK analysis	MANAGEMENT science	EXECUTIVE ability (Management)	DECISION making	NaN	NaN
40	Team Member Functional Background and Involvem...	TEAMS in the workplace	DECISION making	CRITICAL thinking	WORKFLOW	MANAGEMENT	DECENTRALIZATION in management	MANAGEMENT science	ORGANIZATIONAL behavior	DELEGATION of authority	GROUP decision making	STRATEGIC business units	NaN
41	Happy Together? How Using Nonstandard Workers ...	LABOR supply	LABOR organizing	CONDUCT of life	ORGANIZATIONAL behavior	EMPLOYEE loyalty	ORGANIZATIONAL commitment	INDUSTRIAL relations	ORGANIZATIONAL structure	EMPLOYEES -- Attitudes	PERSONNEL management	NaN	NaN
42	Interpersonal Aggression in Work Groups: Socia...	EMPLOYEES -- Attitudes	AGGRESSION (Psychology)	TEAMS in the workplace	SOCIAL influence	INDIVIDUAL differences	INTERPERSONAL relations	SOCIAL context	ORGANIZATIONAL behavior	ORGANIZATIONAL structure	WORK environment	NaN	NaN
43	Share Price Reactions to Work-Family Initiativ...	WORK & family	PERSONNEL management	STOCKHOLDERS	WOMEN employees	STOCKS (Finance) -- Prices	MANAGEMENT science	HUMAN resource accounting	WOMEN -- Employment	ORGANIZATIONAL behavior	QUALITY of work life	NaN	NaN
44	The Role of Human Capital in Postacquisition C...	HUMAN capital	CHIEF executive officers	CAPITAL investments	LABOR economics	CONSOLIDATION & merger of corporations	EXECUTIVES -- Dismissal of	ORGANIZATIONAL effectiveness	ORGANIZATIONAL behavior	LABOR turnover	EXECUTIVE succession	NaN	NaN
48	How Much Should I Give and How Often? The Effe...	SOCIAL status	GENEROSITY	BEHAVIORAL research	LABOR productivity	SOCIAL exchange	INTERPERSONAL relations	SOCIAL factors	EMPLOYEES -- Attitudes -- Research	PERSONNEL management	ORGANIZATIONAL behavior	NaN	NaN
49	Self-Concordance at Work: Toward Understanding...	LEADERSHIP	EXECUTIVE ability (Management)	EMPLOYEE motivation	MOTIVATION (Psychology)	INDUSTRIAL psychology	MANAGEMENT science	JOB satisfaction	CHARISMATIC authority	SELF-congruence	MANAGEMENT styles	NaN	NaN
50	Cooperation, Competition, and Team Performance...	EMPLOYEE motivation	JOB performance	TEAMS in the workplace	INDUSTRIAL management	PERSONNEL management	ORGANIZATIONAL sociology	INCENTIVES in industry	INDUSTRIAL psychology	GOAL setting in personnel management	REWARD (Psychology)	NaN	NaN
51	The Impact Of Expectations On Newcomer Perform...	TEAMS in the workplace	ORGANIZATIONAL sociology	EMPLOYEE motivation	LEADERSHIP	INTERPERSONAL relations	INDUSTRIAL management	PYGMALION (Greek mythology)	GALATEA, sea nymph (Greek deity)	SOCIAL exchange	OCCUPATIONAL roles	NaN	NaN
52	THe Effects of Discontinuous Change on Latent ...	ORGANIZATIONAL change	EMPLOYEE rules	HUMAN error	RISK	INDUSTRIAL management	PERSONNEL management	ORGANIZATIONAL behavior	INDUSTRIAL psychology	ORGANIZATIONAL research	ERROR rates	NaN	NaN
53	Employee Creativity in Taiwan: An Application ...	CREATIVE ability	TAIWANESE	EMPLOYEES	PERSONNEL management	EMPLOYEE motivation	CREATIVE ability in business	INNOVATION management	CROSS-cultural differences	NaN	NaN	NaN	NaN
54	Media Legitimation Effects in the Market for I...	GOING public (Securities)	CORPORATE image	STOCKHOLDERS -- Attitudes	CAPITALISTS & financiers	MASS media	CORPORATIONS -- Investor relations	MATHEMATICAL statistics	CORPORATIONS -- Public relations	PUBLIC companies	TURNOVER (Business)	NaN	NaN
55	Giving Money to Get Money: How CEO Stock Optio...	STOCK options	GOING public (Securities)	INCENTIVES in industry	OPTIONS (Finance)	CORPORATIONS -- Valuation	CORPORATIONS -- Finance	EXECUTIVE compensation	CAPITALISTS & financiers	BUSINESS enterprises -- Valuation	DECISION making	NaN	NaN
59	A Behavioral Theory of R&D Expenditures and In...	ORGANIZATIONAL behavior	CORPORATIONS -- Finance	RESEARCH & development	INDUSTRIAL management	INNOVATIONS in business	INNOVATION management	BUSINESS planning	SHIPBUILDING industry	TECHNOLOGICAL innovations -- Economic aspects	SUCCESS in business	COMPETITIVE advantage	ORGANIZATIONAL change
60	Transformational Leadership, Conservation, and...	LEADERSHIP	ORGANIZATIONAL behavior	CREATIVE ability in business	EMPLOYEE motivation	ORGANIZATIONAL change	WORK environment -- Psychological aspects	MANAGEMENT	EXECUTIVE ability (Management)	INTRINSIC motivation	INDUSTRIAL relations	INDIVIDUAL differences	NaN
61	Informational Dissimilarity and Organizational...	ORGANIZATIONAL behavior	TEAMS in the workplace	INDUSTRIAL psychology	ORGANIZATIONAL effectiveness	ORGANIZATIONAL goals	ORGANIZATIONAL sociology	SOCIAL psychology	MANAGEMENT	ORGANIZATIONAL change	DIVISION of labor	INDUSTRIAL organization	WORK environment
62	Subsidiary Staffing in Multinational Enterpris...	INTERNATIONAL business enterprises -- Management	FOREIGN subsidiaries -- Management	EMPLOYEE selection	EXECUTIVES -- Recruiting	ORGANIZATIONAL sociology	ORGANIZATIONAL behavior	AGENCY theory	RESOURCE-based theory of the firm	PERSONNEL management	EMPLOYMENT in foreign countries	SUBSIDIARY corporations -- Management	HOST countries (Business)
63	Strategic Human Resource Practices, Top Manage...	PERSONNEL management	COMPETITIVE advantage	BUSINESS networks	INDUSTRIAL management	STRATEGIC planning	SOCIAL networks	RESOURCE management	RESOURCE-based theory of the firm	HUMAN capital -- Management	INTELLECTUAL capital	DECISION making	INDUSTRIAL efficiency
64	Compensation Policy and Organizational Perform...	COMPENSATION management	ORGANIZATIONAL behavior	PERSONNEL management	HOSPITALS -- Administration	MANAGEMENT	FINANCIAL performance	WAGE payment systems	RESOURCE management	ORGANIZATIONAL effectiveness	INDUSTRIAL efficiency	FINANCIAL management	INDUSTRIAL management
65	Functional Background Identity, Diversity, and...	CROSS-functional teams	TEAMS in the workplace	GROUP identity	ORGANIZATIONAL behavior	MANAGEMENT	PERFORMANCE	PERSONNEL management	COMPETITIVE advantage	ORGANIZATIONAL effectiveness	GROUP decision making	ORGANIZATIONAL structure	ORGANIZATIONAL sociology
66	A Customer Interaction Approach to Strategy an...	SERVICE industries -- Management	CUSTOMER relations	INDUSTRIAL management	PRODUCTION management	STRATEGIC planning	CUSTOMER services	LABOR process	ORGANIZATIONAL behavior	DECISION making	CUSTOMER satisfaction	CUSTOMER orientation	MARKETING strategy

	Keywords	Degree
23	ORGANIZATIONAL behavior	166
112	ORGANIZATIONAL effectiveness	104
16	MANAGEMENT science	102
20	PERSONNEL management	93
8	DECISION making	90
156	ORGANIZATIONAL structure	74
40	ORGANIZATIONAL sociology	66
88	STRATEGIC planning	66
47	INDUSTRIAL management	64
9	CORPORATE governance	62

	Keywords	Strength
23	ORGANIZATIONAL behavior	265
112	ORGANIZATIONAL effectiveness	144
16	MANAGEMENT science	136
20	PERSONNEL management	126
8	DECISION making	112
156	ORGANIZATIONAL structure	107
40	ORGANIZATIONAL sociology	96
9	CORPORATE governance	85
47	INDUSTRIAL management	84
88	STRATEGIC planning	80

	Keyword-1	Keyword-2	Total_Weight
2492	ORGANIZATIONAL effectiveness	ORGANIZATIONAL behavior	11.0
817	ORGANIZATIONAL behavior	ORGANIZATIONAL effectiveness	11.0
3188	ORGANIZATIONAL structure	ORGANIZATIONAL behavior	9.0
848	ORGANIZATIONAL behavior	ORGANIZATIONAL structure	9.0
749	ORGANIZATIONAL behavior	PERSONNEL management	8.0
614	PERSONNEL management	ORGANIZATIONAL behavior	8.0
457	MANAGEMENT science	ORGANIZATIONAL behavior	7.0
745	ORGANIZATIONAL behavior	MANAGEMENT science	7.0
2554	ORGANIZATIONAL effectiveness	ORGANIZATIONAL structure	6.0
183	DECISION making	ORGANIZATIONAL behavior	6.0

	Degree	Strength
1	4	4.000000
2	7	7.000000
3	8	8.000000
4	9	9.000000
5	10	10.000000
6	11	11.000000
7	14	14.500000
8	15	16.000000
9	16	18.200000
10	17	18.153846
11	18	19.200000
12	19	20.500000
13	20	20.400000
14	21	21.666667
15	23	27.500000
16	24	27.666667
17	25	27.000000
18	26	30.250000
19	27	30.000000
20	28	30.000000
21	29	31.000000
22	31	37.000000
23	32	36.000000
24	35	44.000000
25	37	45.500000
26	38	48.000000
27	41	53.000000
28	42	47.000000
29	46	57.000000
30	48	60.000000
31	49	59.000000
32	55	78.000000
33	62	85.000000
34	64	84.000000
35	66	88.000000
36	74	107.000000
37	90	112.000000
38	93	126.000000
39	102	136.000000
40	104	144.000000
41	166	265.000000

FDA Project- 3¶

Submission: 12/15/2022¶

Surya Pratap Singh¶

Task-1¶

Task 2¶

	Title	Keyword 1	Keyword 2	Keyword 3	Keyword 4	Keyword 5	Keyword 6	Keyword 7	Keyword 8	Keyword 9	Keyword 10	Keyword 11	Keyword 12
1	Feb/03	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	Meta-Analyses of Financial Performance and Equ...	EQUITY	ORGANIZATIONAL sociology	PERFORMANCE	META-analysis	PSYCHOMETRICS	ORGANIZATIONAL research	FINANCIAL performance	AGENCY theory	ORGANIZATIONAL effectiveness	ORGANIZATIONAL behavior	CORPORATE governance	NaN
4	Home Country Environments, Corporate Diversifi...	DIVERSIFICATION in industry	BUSINESS planning	PERFORMANCE standards	EMPLOYEES -- Rating of	CORPORATE culture	STRATEGIC planning	ORGANIZATIONAL effectiveness	MANAGEMENT science	MANAGEMENT research	PRODUCT management	NaN	NaN
5	Safeguarding Investments in Asymmetric Interor...	INTERORGANIZATIONAL relations	INTERGROUP relations	BUSINESS communication	INVESTMENTS	SUPPLY chains	KNOWLEDGE management	INTERORGANIZATIONAL networks	CORPORATE governance	GROUP decision making	INTELLECTUAL capital	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...
62	Subsidiary Staffing in Multinational Enterpris...	INTERNATIONAL business enterprises -- Management	FOREIGN subsidiaries -- Management	EMPLOYEE selection	EXECUTIVES -- Recruiting	ORGANIZATIONAL sociology	ORGANIZATIONAL behavior	AGENCY theory	RESOURCE-based theory of the firm	PERSONNEL management	EMPLOYMENT in foreign countries	SUBSIDIARY corporations -- Management	HOST countries (Business)
63	Strategic Human Resource Practices, Top Manage...	PERSONNEL management	COMPETITIVE advantage	BUSINESS networks	INDUSTRIAL management	STRATEGIC planning	SOCIAL networks	RESOURCE management	RESOURCE-based theory of the firm	HUMAN capital -- Management	INTELLECTUAL capital	DECISION making	INDUSTRIAL efficiency
64	Compensation Policy and Organizational Perform...	COMPENSATION management	ORGANIZATIONAL behavior	PERSONNEL management	HOSPITALS -- Administration	MANAGEMENT	FINANCIAL performance	WAGE payment systems	RESOURCE management	ORGANIZATIONAL effectiveness	INDUSTRIAL efficiency	FINANCIAL management	INDUSTRIAL management
65	Functional Background Identity, Diversity, and...	CROSS-functional teams	TEAMS in the workplace	GROUP identity	ORGANIZATIONAL behavior	MANAGEMENT	PERFORMANCE	PERSONNEL management	COMPETITIVE advantage	ORGANIZATIONAL effectiveness	GROUP decision making	ORGANIZATIONAL structure	ORGANIZATIONAL sociology
66	A Customer Interaction Approach to Strategy an...	SERVICE industries -- Management	CUSTOMER relations	INDUSTRIAL management	PRODUCTION management	STRATEGIC planning	CUSTOMER services	LABOR process	ORGANIZATIONAL behavior	DECISION making	CUSTOMER satisfaction	CUSTOMER orientation	MARKETING strategy

	year	tweet
0	2020	[entertainment, critical, cars, drive]
1	2020	[meeting, larry, ellison, seek, advice, back, ...
2	2020	[absolutely]
3	2020	[tesla, incar, gaming, autonomous, world]
4	2020	[absolutely]
...	...	...
15852	2022	[reminds, hex, edited, ultima, v, get, final, ...
15853	2022	[yay, switzerland]
15854	2022	[way, touch, voters, youre, three, generations...
15856	2022	[lets, make, roaring, 20s, happen]
15857	2022	[great, work, tesla, team, worldwide]

	year	tweet
0	2020	entertainment
0	2020	critical
0	2020	cars
0	2020	drive
1	2020	meeting
...	...	...
15857	2022	great
15857	2022	work
15857	2022	tesla
15857	2022	team
15857	2022	worldwide

	year	tweet	num	rank
26628	2022	tesla	62	1.0
26273	2022	people	34	2.0
26821	2022	yes	32	3.0
26218	2022	one	30	4.0
26809	2022	would	30	5.0
...	...	...	...	...
3251	2017	youth	1	3254.0
3252	2017	yrs	1	3255.0
3254	2017	zedd	1	3256.0
3256	2017	zeroth	1	3257.0
3257	2017	zootopia	1	3258.0

	year	tweet	num	rank	total	term frequency
0	2017	0	3	534.0	8657	0.000347
1	2017	008	1	1343.0	8657	0.000116
2	2017	015	1	1344.0	8657	0.000116
3	2017	040	1	1345.0	8657	0.000116
4	2017	07m	1	1346.0	8657	0.000116
...	...	...	...	...	...	...
26825	2022	youre	4	477.0	5326	0.000751
26826	2022	youtube	2	1526.0	5326	0.000376
26827	2022	yup	10	83.0	5326	0.001878
26828	2022	zealand	2	1527.0	5326	0.000376
26829	2022	zukunft	2	1528.0	5326	0.000376